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System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

What difficulty level is this interview question?

This is a hard difficulty System Design question, commonly asked during Onsite rounds at Uber.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at Uber during technical interviews.

Design MapReduce for schedules | Uber Interview Question

Quick Overview

This question evaluates proficiency in scalable stream and batch data processing, windowed aggregation, event-time semantics, deduplication, skew mitigation, and distributed computation using MapReduce/Spark paradigms; it falls under System Design and Big Data/stream-processing and is commonly asked to assess designing correct, high-throughput windowed aggregations and handling late or duplicated events at scale. The level of abstraction is practical application with architectural and operational considerations rather than low-level coding, and this English summary frames the competency and interview focus for search engines.

Design a scalable job to compute rolling 15-minute meeting concurrency and room utilization

You are given a massive log of meeting events with fields:

user_id
start_time (UTC)
end_time (UTC)
room_id
office_id

Compute, for each rolling 15-minute window (assume slide = 1 minute unless specified otherwise):

Concurrent meeting counts per office (report peak concurrent meetings within each 15-minute window).
Rooms whose utilization exceeds 80% within the window, where utilization = occupied_time_in_window / 900 seconds.

Design a MapReduce or Spark job that:

Specifies map keys/values and how you transform intervals into windowed aggregates (e.g., line-sweep with +1/−1 at boundaries vs. interval splitting into windows).
Describes the shuffle/partition strategy to minimize skew and bound reducer work.
Explains handling of late and duplicated events (event-time watermarks, dedup keys, update/retraction strategy).
Proposes validation and correctness checks at scale.

Make minimal, explicit assumptions if needed (e.g., presence of meeting_id for dedup, window slide granularity).

Quick Overview

Design a scalable job to compute rolling 15-minute meeting concurrency and room utilization

You are given a massive log of meeting events with fields:

user_id

start_time (UTC)

end_time (UTC)

room_id

office_id

Compute, for each rolling 15-minute window (assume slide = 1 minute unless specified otherwise):

Concurrent meeting counts per office (report peak concurrent meetings within each 15-minute window).

Rooms whose utilization exceeds 80% within the window, where utilization = occupied_time_in_window / 900 seconds.

Design a MapReduce or Spark job that:

Specifies map keys/values and how you transform intervals into windowed aggregates (e.g., line-sweep with +1/−1 at boundaries vs. interval splitting into windows).

Describes the shuffle/partition strategy to minimize skew and bound reducer work.

Explains handling of late and duplicated events (event-time watermarks, dedup keys, update/retraction strategy).

Proposes validation and correctness checks at scale.

Make minimal, explicit assumptions if needed (e.g., presence of meeting_id for dedup, window slide granularity).

Design MapReduce for schedules

Quick Overview

Design a scalable job to compute rolling 15-minute meeting concurrency and room utilization

Solution

Comments (0)

Design MapReduce for schedules

Quick Overview

Design a scalable job to compute rolling 15-minute meeting concurrency and room utilization

Solution

Comments (0)